arxiv: 2605.01668 · v1 · submitted 2026-05-03 · 💻 cs.CV · cs.AI

Recognition: unknown

IMPACT-Scribe: Interactive Temporal Action Segmentation with Boundary Scribbles and Query Planning

Qian Yin , Di Wen , Kunyu Peng , David Schneider , Zeyun Zhong , Alexander Jaus , Zdravko Marinov , Jiale Wei

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Ruiping Liu Junwei Zheng Yufan Chen Chen Zhang Lei Qi Rainer Stiefelhagen

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:11 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords temporal action segmentationinteractive annotationboundary scribblesquery planningcorrection-driven adaptationhuman-machine collaborationvideo labelingprocedural activities

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The pith

IMPACT-Scribe reuses each human correction to plan better queries and adapt the model, raising label quality per effort in video action segmentation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that current annotation tools waste information by treating each correction as an isolated edit. IMPACT-Scribe instead creates a closed loop that feeds corrections back through uncertainty-aware boundary scribbles, cost-aware query planning, structured propagation, and model adaptation. A sympathetic reader would care because dense labeling of procedural videos remains a major bottleneck for training systems that understand human activities. The authors back the approach with experiments and a human study that measure gains in quality, boundary accuracy, and long-term collaboration.

Core claim

IMPACT-Scribe is a correction-driven framework for interactive temporal action segmentation that combines uncertainty-aware boundary scribble supervision, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation to convert each human correction into reusable knowledge that improves future human-machine collaboration.

What carries the argument

The closed-loop correction-driven framework that integrates boundary scribbles with query planning and adaptation to reuse information from each human edit across labeling rounds.

Load-bearing premise

Human corrections reliably supply reusable signals about uncertainty and model reliability that the system's components can integrate without introducing new errors or biases.

What would settle it

A human study comparing IMPACT-Scribe to a standard reactive tool in which no measurable improvement appears in label quality, boundary accuracy, or time per annotation would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.01668 by Alexander Jaus, Chen Zhang, David Schneider, Di Wen, Jiale Wei, Junwei Zheng, Kunyu Peng, Lei Qi, Qian Yin, Rainer Stiefelhagen, Ruiping Liu, Yufan Chen, Zdravko Marinov, Zeyun Zhong.

**Figure 1.** Figure 1: Overview of the IMPACT-Scribe system. Given an input video, a frozen feature extractor produces dense embeddings that support five core components. Uncertainty-Aware Scribble Encoding (USE, §III-B) converts annotator scribbles into a 3-channel temporal signal (uncertain / left / right). The Local Proposal Model (§III-C) predicts boundary corrections from the encoded scribble and dense features. Cost-Aware … view at source ↗

**Figure 3.** Figure 3: shows that removing any single component degrades boundary quality per interaction step relative to the full system across all 135 IMPACT test cases. Ablation. Table III reports component ablations under a fixed interaction budget. Local proposal ablations test whether gains come from learned local correction inference and consistency-aware training; system-level ablations test whether query planning, adap… view at source ↗

**Figure 4.** Figure 4: Repeated correction with structured propagation. V. CONCLUSION Dense temporal annotation has long been treated as a throughput problem. IMPACT-Scribe reframes it as a collaboration problem: the more effectively a system can learn from each correction, the better it can allocate future assistance and convert human effort into annotation quality. By treating every accepted edit simultaneously as an update t… view at source ↗

read the original abstract

Dense temporal annotation of procedural activity videos is vital for action understanding and embodied intelligence but remains labor-intensive due to reactive tools. Each correction is treated as an isolated edit, limiting reuse of information on annotator uncertainty and model reliability. We introduce IMPACT-Scribe, a correction-driven framework for dense labeling that uses each correction to improve future human-machine collaboration. IMPACT-Scribe combines uncertainty-aware boundary scribble supervision, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation. Experiments and a human study show that this closed-loop design improves labeling quality per effort, enhances boundary accuracy, and fosters better human-machine interaction over time. The code will be made publicly available at https://github.com/BanzQians/IMPACT_AS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces a closed-loop interactive annotation framework for temporal action segmentation that reuses corrections via uncertainty-aware scribbles and query planning, but the abstract supplies no metrics or controls to judge whether the claimed gains are real.

read the letter

The core idea here is treating human corrections as ongoing feedback that lets the system get better at suggesting boundaries and queries over time, rather than handling each edit in isolation. This is presented as new through the specific mix of uncertainty-aware boundary scribbles, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation for dense labeling of procedural videos. The work does address a real bottleneck in training action-understanding models for embodied AI, where dense annotation is expensive, and it tries to make the human-machine loop more efficient by design. The abstract states that experiments plus a human study back this up with better labeling quality per effort and improved boundary accuracy. That framing is straightforward and the components are clearly motivated from the problem description. The main soft spot is that none of those improvements are quantified—no numbers, no baselines, no ablation results, and no description of the human-study design. Without those, it is impossible to tell how large the gains are or whether they come from the adaptation mechanism. The stress-test point about practice effects lands: if the study lacked a non-adaptive control arm using the same scribble interface, then annotators simply getting familiar with the task could explain any improvement across sessions. The assumption that each correction reliably supplies reusable information on uncertainty without adding bias therefore remains untested in what is visible. This paper is for researchers working on interactive video annotation tools and temporal segmentation. A reader interested in practical ways to cut labeling costs would find the framework description useful as a starting point, but would need the full experimental section before deciding whether to try the approach. It deserves peer review because the problem is important and the closed-loop framing is concrete, even if the current write-up is high-level and the evidence details are missing.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces IMPACT-Scribe, a correction-driven interactive framework for dense temporal action segmentation of procedural videos. It integrates uncertainty-aware boundary scribble supervision, local proposal modeling, cost-aware query planning, structured propagation, and correction-driven adaptation to reuse information from each human correction for improved future collaboration. The central claim, supported by experiments and a human study, is that this closed-loop design yields better labeling quality per effort, higher boundary accuracy, and improved human-machine interaction over time, with code to be released publicly.

Significance. If the empirical results hold under proper controls, the work could reduce annotation effort for dense procedural activity labels, a key bottleneck in action understanding and embodied AI. The explicit reuse of correction signals and public code commitment are strengths that aid reproducibility and extension.

major comments (2)

[Human study] Human study section: the evaluation does not describe a non-adaptive control arm that employs the identical scribble interface and query mechanism across sessions while disabling correction-driven adaptation. Without this, observed gains in labeling quality and boundary accuracy cannot be unambiguously attributed to the proposed closed-loop components rather than annotator familiarization or practice effects, directly undermining the central claim that each correction supplies reusable uncertainty information.
[Experiments] Experiments section: the abstract and manuscript summary assert quantitative improvements from experiments yet provide no concrete metrics (e.g., mIoU, boundary F1), baselines, ablation tables, dataset splits, or statistical significance tests. This absence makes it impossible to assess whether the data support the claimed gains in quality-per-effort or interaction quality.

minor comments (1)

[Abstract] The abstract states that 'the code will be made publicly available' but does not specify the exact release timing or repository link beyond the GitHub placeholder; this should be clarified for reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We have reviewed the major comments carefully and provide point-by-point responses below. Where the comments identify gaps in controls or presentation of results, we agree and outline specific revisions to strengthen the paper.

read point-by-point responses

Referee: [Human study] Human study section: the evaluation does not describe a non-adaptive control arm that employs the identical scribble interface and query mechanism across sessions while disabling correction-driven adaptation. Without this, observed gains in labeling quality and boundary accuracy cannot be unambiguously attributed to the proposed closed-loop components rather than annotator familiarization or practice effects, directly undermining the central claim that each correction supplies reusable uncertainty information.

Authors: We agree this is a valid methodological concern. The human study section as written does not explicitly describe or include a non-adaptive control arm that uses the same scribble interface and query mechanism but disables correction-driven adaptation. This leaves open the possibility that observed improvements partly reflect practice effects. We will revise the manuscript to add a dedicated control-arm comparison (either via new sessions or re-analysis of existing data where adaptation is turned off) and report the resulting differences in labeling quality and boundary accuracy. The revised text will qualify the central claim accordingly and include this controlled evidence. revision: yes
Referee: [Experiments] Experiments section: the abstract and manuscript summary assert quantitative improvements from experiments yet provide no concrete metrics (e.g., mIoU, boundary F1), baselines, ablation tables, dataset splits, or statistical significance tests. This absence makes it impossible to assess whether the data support the claimed gains in quality-per-effort or interaction quality.

Authors: We acknowledge that the abstract and high-level summary do not present specific numerical results, which makes it difficult for readers to evaluate the claims. Although the full experiments section contains quantitative evaluations, we will revise the manuscript to add an explicit summary table of key metrics (mIoU, boundary F1), baseline comparisons, ablation results on each component, dataset split details, and statistical significance tests (e.g., p-values). We will also update the abstract to reference concrete improvements in quality-per-effort and interaction quality. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical claims rest on external human input and study validation

full rationale

The paper describes an interactive framework whose core components (uncertainty-aware scribbles, query planning, propagation, adaptation) take human corrections as external inputs rather than deriving outputs from fitted parameters or self-referential definitions. No equations, parameter fits, or uniqueness theorems appear in the provided text that would reduce the claimed labeling improvements to the framework's own inputs by construction. Validation is performed via separate experiments and a human study, which constitute independent evidence outside the system definition itself.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, hyperparameters, or explicit assumptions; therefore no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5466 in / 1205 out tokens · 53766 ms · 2026-05-10T16:11:14.249696+00:00 · methodology

discussion (0)

Reference graph

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